roc | A Modern Finagle-Postgresql Client | Grid library

I want to be able to do lots of AUCs at once from the pROC package. Here is a simple dataframe with two predictors and a binary outcome and my attempt to use sapply() along with auc() and roc() from the pROC library. What am I doing wrong?

I am trying to perform a sentiment analysis based on product reviews collected from various websites. I've been able to follow along with the below article until it gets to the model coefficient visualization step.

https://towardsdatascience.com/how-a-simple-algorithm-classifies-texts-with-moderate-accuracy-79f0cd9eb47

When I run my program, I get the following error:

I have a class Rigmodel, inside class RigModel I have RigDetails class as a properties. I initialize RigDetails in constructor of RigModel as below.

I have this dataset with target LULUS, it's an imbalance dataset. I'm trying to print roc auc score if I could for each fold of my data but in every fold somehow it's always raise error saying ValueError: y should be a 1d array, got an array of shape (15, 2) instead.. I'm kind of confused which part I did wrong because I do it exactly like in the documentation. And in several fold, I get it that It won't print the score if there's only one label but then it will return the second type of error about 1d array.

I want to build a heatmap where on Y-axis will be number of trees, on X number of leafs, and in the center auc-roc Here is my code

I have a question concerning survival analysis. However, I have the following data (just an excerpt):

Now I am trying to do Survival Analysis with Python lifelines package. For example I want to find out if T-cells influence the Overall Survival (OS). But as far as I know, I need to categorizie the numer of T cells in different categories, like e.g. High T-Cell and Low T-Cell... Is that right? But how do I find out the best fitting Cut-Out? My plan is to show, that Tumor with High T-Cells have a better survival than low T-Cells. But how could I find the best cut-off-value to discriminate between High and Low T-Cell out of the data I have here.

Does anyone has an idea? A friend of mine said something about "ROC"-Analysis but I am really confused now... I would be glad about any help!

I am trying to create a function to extraction of confidence intervals for 2 named variables of an 'area under the curve' estimate using the ci.auc() function from the pROC package, but it produces and error: Error in model.frame.default(formula = anchor, data = namedvar1, : 'data' must be a data.frame, environment, or list . How to can Ifix this? Is there a better way to specify which dataframe to draw the named variables from?

The original code works fine:

I am trying to plot ROC curve in sklearn. The results below are the predicted values (y_score) and the labels (labels). Is it possible to plot roc_curve from that or must one have the raw probabilities?

I have a table holdouTable with the column of prediction pred.

library(precrec)

head(holdoutTable)

enter image description here Species geometry pred 1 1 POINT (38.80153 54.88964) 0.33363164 2 1 POINT (37.83133 55.701) 0.56814105 3 1 POINT (36.48579 55.71794) 0.09608355 4 1 POINT (37.54231 55.58439) 0.10376186 5 1 POINT (37.3788 54.9587) 0.07254712 6 1 POINT (37.40738 55.00861) 0.11294458

I calculate ROC AUC and plot it, but I do not know how to get confusion matrix? Could anybody to help me?

holdotTable$pred <- predict(mx, hddata, type = "cloglog")

precrec_hd <- evalmod(scores = holdotTable$pred, labels = holdotTable$Species)

autoplot(precrec_hd, curvetype = "ROC")

precrec_hd

Model name Dataset ID Curve type AUC 1 m1 1 ROC 0.8434492 2 m1 1 PRC 0.7676958

P.S. Previously people asked me to put output of code instead screenshot of my data. I am not sure that I do it successful now, because I just copy form console and paste (I putted image of holdoutTable in any case). If you can advise me how to put it in right way,I will be glad to know. Thanks!